Building sentiment lexicons based on recommending services for the Polish language

Bogdan Gliwa; Anna Zygmunt; Michał Dąbrowski

doi:10.7494/csci.2016.17.2.163

Authors

Bogdan Gliwa AGH University of Science and Technology, Department of Computer Science, Krakow
Anna Zygmunt AGH University of Science and Technology, Department of Computer Science, Krakow
Michał Dąbrowski AGH University of Science and Technology, Department of Computer Science, Krakow

DOI:

https://doi.org/10.7494/csci.2016.17.2.163

Keywords:

sentiment analysis, sentiment lexicons, polarity lexicons, sentiment classification

Abstract

Sentiment analysis has become a prominent area of research in computer science. It has numerous practical applications; e.g., evaluating customer satisfaction, identifying product promoters. Many methods employed in this task require language resources such as sentiment lexicons, which are unavailable for the Polish language. Such lexicons contain words annotated with their emotional polarization, but the manual creation of sentiment lexicons is very tedious. Therefore, this paper addresses this issue and describes a new method of building sentiment lexicons automatically based on recommending services. Next, the built lexicons were used in the task of sentiment classification.

Downloads

Download data is not yet available.

References

Aggarwal C., Zhai C.: A survey of text classification algorithms. In: C. Aggarwal, C. Zhai, eds., Mining Text Data, pp. 163–222. Springer, 2012.

Anjaria M., Guddeti R.: A novel sentiment analysis of social networks using supervised learning. In: Social Network Analysis and Mining, vol. 4(1), 181, 2014. ISSN 1869-5450. URL http://dx.doi.org/10.1007/s13278-014-0181-9.

Blei D.: Probabilistic Topic Models. In: Communications of the ACM, vol. 55(4), pp. 77–84, 2012.

Blei D., Lafferty J.: Dynamic topic models. In: Proceedings of the 23rd international conference on Machine learning, p. 113120. 2006.

Blei D., Ng A., Jordan M.: Latent Dirichlet allocation. In: Journal of Machine Learning Research, vol. 3, p. 9931022, 2003.

Chaovalit P., Zhou L.: Movie Review Mining: A Comparison Between Supervised and Unsupervised Classification Approaches. In: Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS’05), HICSS ’05. IEEE Computer Society, 2005.

Crain S., Zhou K., Yang S., Zha H.: Dimensionality reduction and Topic Modelling: from Latent Semantic Indexing to Latent Dirichlet Allocation and beyond. In: C. Aggarwal, C. Zhai, eds., Mining Text Data, pp. 129–162. Springer, 2012.

Denecke K.: Using SentiWordNet for multilingual sentiment analysis. In: Proceedings of the 24th International Conference on Data Engineering Workshops, pp. 507–512. IEEE Computer Society, 2008.

Feldman R.: Techniques and Applications for Sentiment Analysis. In: Commun. ACM, vol. 56(4), pp. 82–89, 2013. ISSN 0001-0782. URL http://dx.doi.org/10.1145/2436256.2436274.

Harish B., Guru D., Manjunath S.: Representation and classification of text documents: A brief review. In: IJCA, Special Issue on RTIPPR (2), pp. 110–119, 2010.

Hatzivassiloglou V., McKeown K.R.: Predicting the Semantic Orientation of Adjectives. In: Proceedings of the Eighth Conference on European Chapter of the Association for Computational Linguistics, EACL ’97, pp. 174–181. Association for Computational Linguistics, Stroudsburg, PA, USA, 1997. URL http://dx.doi.org/10.3115/979617.979640.

Hotho A., Nrnberger A., Paa G.: A brief survey of text mining. In: LDV Forum - GLDV Journal for Computational Linguistics and Language Technology, 2005.

Huang Y.: Support vector machines for text categorization based on latent semantic indexing. Tech. rep., Electrical and Computer Engineering Department, The Johns Hopkins University, 2003.

Kamps J., Marx M., Mokken R.J., Rijke M.D.: Using wordnet to measure semantic orientation of adjectives. In: National Institute for, pp. 1115–1118. 2004.

Kim S.M., Hovy E.: Determining the Sentiment of Opinions. In: Proceedings of the 20th International Conference on Computational Linguistics, COLING ’04. Association for Computational Linguistics, Stroudsburg, PA, USA, 2004. URL http://dx.doi.org/10.3115/1220355.1220555.

Li G., Liu F.: Application of a Clustering Method on Sentiment Analysis. In: Journal of Information Science, vol. 38(2), pp. 127–139, 2012. ISSN 0165-5515. URL http://dx.doi.org/10.1177/0165551511432670.

Liu B.: Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers, 2012. URL http://dx.doi.org/10.2200/S00416ED1V01Y201204HLT016.

Mudambi S.M., Schuff D.: What makes a helpful online review? A study of customer reviews on Amazon.com. In: MIS Quarterly, pp. 185–200. 2010.

Mullen T., Collier N.: Sentiment Analysis using Support Vector Machines with Diverse Information Sources. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 412–418. 2004.

Niu Z., Yin Z., Kong X.: Sentiment Classification for Microblog by Machine Learning. In: Proceedings of the 2012 Fourth International Conference on Computational and Information Sciences, pp. 286–289. IEEE Computer Society, 2012.

Pak A., Paroubek P.: Twitter for Sentiment Analysis: When Language Resources are Not Available. In: Database and Expert Systems Applications (DEXA), 2011 22nd International Workshop on, pp. 111–115. 2011. ISSN 1529-4188. URL http://dx.doi.org/10.1109/DEXA.2011.86.

Pang B., Lee L.: Opinion mining and sentiment analysis. In: Foundations and Trends in Information Retrieval, vol. 2(1-2), 2008.

Pang B., Lee L., Vaithyanathan S.: Thumbs Up?: Sentiment Classification Using Machine Learning Techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10, pp. 79–86. Association for Computational Linguistics, 2002.

Peng T.C., Shih C.C.: An Unsupervised Snippet-Based Sentiment Classification Method for Chinese Unknown Phrases without Using Reference Word Pairs. In: Web Intelligence/IAT Workshops, pp. 243–248. IEEE, 2010.

Ramos J.: Using tf-idf to determine word relevance in document queries. In: Proceedings of the first instructional conference on machine learning. 2003.

Rennie J.D., Shih L., Teevan J., Karger D.R.: Tackling the Poor Assumptions of Naive Bayes Text Classifiers. In: ICML, pp. 616–623. AAAI Press, 2003.

Tromp E., Pechenizkiy M.: SentiCorr: Multilingual Sentiment Analysis of Personal Correspondence. In: Proc. of ICDM 2011 Workshops. IEEE Press, 2011.

Turetken O., Olfman L.: Introduction to the Special Issue on Human-Computer Interaction in the Web 2.0 Era. In: AIS Transactions on Human-Computer Interaction, pp. 1–5. 2013.

Turney P.D.: Thumbs Up or Thumbs Down?: Semantic Orientation Applied to Unsupervised Classification of Reviews. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, ACL ’02, pp. 417–424. Association for Computational Linguistics, Stroudsburg, PA, USA, 2002. URL http://dx.doi.org/10.3115/1073083.1073153.

Vinodhini G., Chandrasekaran R.M.: Sentiment Analysis and Opinion Mining: A Survey. In: International Journal, vol. 2(6), 2012.

X. Hu H.L.: Text analytics in social media. In: C.C. Aggarwal, C. Zhai, eds., Mining Text Data, pp. 385–414. Springer, 2012.

Zhang H., Yu Z., Xu M., Shi Y.: An Improved Method to Building a Score Lexicon for Chinese Sentiment Analysis. In: SKG, pp. 241–244. IEEE Computer Society, 2012.